accuracy in the hands of the workforce_portable gps devices prove central to accurate solutions
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Accuracy in the
Hands of theWorkforce:
Portable GPS Devices
Prove Central to
Accurate Solutions
May 2011
This report was researched and prepare
by Vector1 Media with support an
sponsorship from Trimble
Research
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Executive Overview
The casual use of satellite-based positioning has proliferated, with GPS now readily available
in mobile phones and cars. Most GPS receiver chips in such devices can achieve an accuracy of
just two to three meters, which is far less accurate than professional-grade devices. Consumer-
grade devices lack the ability to provide quality assurance details or metadata about position so
that you cant be sure with confidence that the positions that you collected are correct. When
lower-end accuracy is used for data collection, what also gets lost is the utility of high-accuracy
position, and the kinds of insight that are revealed only at higher accuracies.
Businesses increasingly understand that spatial awareness of workers, assets and processes
can lead to much greater efficiencies. But the best enterprise-wide information system can be
sabotaged by data that is not accurate enough. The bottom line of garbage in, garbage out isas relevant today as it was when the first database was invented. Poor positional accuracy, and
in some cases wrong positions, degrade the utility of your systems, and can even result in costly
errors.
Given the benefit of greater accuracy, the trend across the GIS and mapping arena is toward
higher accuracy data collection. This began with aerial and satellite imagery as pixels the size
of a meter squared rapidly improved to the 50 cm range. Similarly, we have high-speed LiDAR
collection tools that capture large areas at 5 to 10 cm accuracy. Were now entering a realm
where highly portable handheld devices can achieve accuracy that was once only possible with
survey-grade tools.
The improved portable high-accuracy capability is potentially a game changer. It puts high
precision into the hands of a greater percentage of the workforce, which can improve the quality
of spatial information in an organization to unlock new efficiencies and insight.
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IntroductionAmong the positioning community, accuracy is a moving
feast. There are plenty of applications where accuracies of a
few kilometers have been deemed adequate, while there are
others where a few millimeters are not accurate enough.
Historically, precise measurement required more care (so
more time), required higher quality (so more expensive)
instruments and led to the generation of more data (so bigger
data sets). This last is not a trivial consideration, since the
size of spatial data sets can multiply exponentially as more
accurate recordings of position are taken. This in turn set off a
new round of expenditure on hardware to store the data, and
software to process and interpret it.
The exciting news is that this situation has changed. The latestrange of equipment now available is incredibly accurate, and
lower in price, more reliable and easy to use. On todays work
sites, people with many different skill sets, not just surveyors,
can be involved in collecting high resolution data. And, they
often integrate it with other kinds of geospatial data to gain
increased value.
The role of professional surveyors is increasingly centered on
designing data collection methodologies, building databases
and ensuring that the data that is being generated by the
system is fit for use by the people who are going to use it.
This had led surveyors and other generators of spatial data
to considerations of two closely related concepts: fitness for
purpose and return on investment .
The concept of fitness for purpose supposes that, for any
given spatial problem, an experienced professional can
develop metrics that say: this problem can be solved with
data that has these characteristics. That may be a statement
about accuracy. So for instance, an engineering surveyor
considering the position of piles for a new bridge may find
positions plus or minus 10 mm hard to live with. An asset
manager, on the other hand, who is hunting for an existingsewer main with a backhoe, might think that any accuracy
within the size of the bucket of the backhoe (say a meter) is
good enough. And an environmental manager making an
inventory of trees might think that plus or minus five meters is
perfectly adequate.
But increasingly, the solution is more than accuracy alone,
since accuracy is now so easy to achieve. Instead, the shift is
toward adding value that leads to higher return on investment.
A data acquisition plan must be developed that balances costs
against the potential uses of the data. Can the end users be
clearly defined? In some cases, the answer will be obvious.
In others, it will be less clear. A balance between accuracy,
time and cost in relationship to value-added use provides
important guidance. And when considering use, the growing
trend is toward understanding and estimating the entire data
delivery ecosystem including storage and delivery.
This new approach ought to include a mechanism for
introducing risk management related to the value of the data.
How much will it cost me if the data is wrong? How much
re-work will I have to do? What impact will accuracy have on
staff productivity? How do we expect the data to perform in
crisis situations where poor accuracy could mean loss of life?
A further consideration concerns the management of the data
is whether data collected for one purpose can be used for
others? Might collecting data at higher accuracy mean that it
is fit for more than one purpose? If so, does this change how
much I am prepared to pay for it?
New positioning technology is helping to solve parts of this
puzzle, and will continue to do so in the future. Technology
innovation is now providing improved accuracy and
performance for lower prices, making the collection of
higher accuracy positions a more attractive option for all
applications, with significant productivity benefits acrossentire organizations.
The Role of AccuracyThe quality of the geospatial data starting point has great
bearing on what can be done with the data and where it
can be applied. Management strategies that include an
understanding of individual applications, together with
selection of the appropriate geospatial technology, will
achieve greater effectiveness and efficiency across all
operations.
Spatial data quality is directly related to the type ofmeasurement equipment being used and the application for
which the data was initially collected. The complexity and
difficulty of using survey and measurement instruments for
high-accuracy collection is being replaced with easy-to-use
devices and interfaces that collect better positional accuracy at
reduced price points.
A sound beginning strategy for determining the many factors
related to position collection recognizes the accuracy - use
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connection. Accuracy and precision are very important when
integrating survey data with other kinds of geospatial data
because they often form the lowest or baseline tier upon
which all other geospatial workflows and data will build upon.
Lower quality data may be sufficient to verify a position or
mark a completed task to a location, but not to update or
change the position of the asset, especially when the initial
position was derived from a high-accuracy source.
The operational use of geospatial data across a city, region
or utility often accesses a seamless database whose records
are expected to be of the highest quality available. The
close management and stewardship of accuracy within this
database is an ongoing challenge where accuracy can be too
easily degraded by poor field data collection practices, inferior
equipment, non-standardized practices, and lack of oversight.
Technology increasingly addresses these points of failure
by reducing complexity and automating accuracy assurance
processes.
The degree of data accuracy relates to how different data
types can be combined and used with each other. Achieving
multi-functional capability with the same system for multiple
applications requires high-accuracy data, thus the accuracy to
quality relationship is critical to realize maximum value from
the data collection effort.
Innovations to Achieve High-AccuracyField data collection has benefitted greatly from the GlobalPositioning System (GPS) constellation of navigational and
location satellites operated by the United States government.
The term GPS is quickly being supplanted with the term
Global Navigation Satellite System (GNSS) as other nations
have joined in setting up their own sat nav systems. Other
initiatives include Russia (GLONASS), Europe (GALILEO),
China (BEIDOU or Compass) and Japan (QZSS).
All GNSS work by measuring the distance between the
satellites and the receiver. They do this by timing when the
signals leave the satellite and when they are received at thereceiver. For instance, the coarse-acquisition (C/A) code sent
on the GPS sends a bit every 0.98 microseconds, so a receiver
is accurate to 0.01 microsecond, which equates to about
3 metres. This is the best possible accuracy of a standard
unaided receiver.
Field data collection handheld manufacturers have responded
to these new constellations by creating receivers that can
receive signals from more than one constellation. Using two
or more constellations results in increased availability of
satellite signals, improving accuracy and reliability, particularly
in difficult environments such as inner city environments and
under heavy vegetation.
Exclusive reliance on orbiting satellites for position has its
limitations, since atmospheric conditions and surroundings
can degrade positional accuracy. Ground-based efforts to
improve accuracy use differential correction by processing
signal errors collected at a known position or base station,
then applying these corrections to individual operational
GNSS receivers. This approach requires two receivers, the
collection and or processing of errors and the transfer of these
to the field receiver. Accuracy may be improved to submeter
accuracy in many cases.
Another ground-based approach to improve positional
accuracy is the real-time kinematic (RTK) surveying approach.
Trimbles Virtual Reference Station (VRS) service is an
example of an RTK network that models corrections and
differences in signals between many fixed base stations.
As field devices move throughout the field site, modeled
corrections are continually transferred to these receivers.
Obstructions of satellite signals effectively cast a shadow
over the GNSS receiver, either degrading the satellite signal
strength or completely blocking the signal and stopping
it from being tracked at all. Trimble has a introduced anew technology to mitigate the effects of satellite shadow.
Trimble Floodlight satellite shadow reduction technology
approaches the problem of loss of GNSS productivity by
increasing the sensitivity of the receiver to better perform with
weak signals using barometer-based altitude measurements
to reduce degraded signals and to replace missing signals
during temporary outages and increasing the number of
satellites in view by adding GLONASS support.
Many countries have created continually operating reference
stations (CORS). These networks provide wide region
correction data for use with high-accuracy GNSS fieldequipment.
Survey-grade, highly accurate receivers usually work by
computing the phase of the radio frequency carrier emitted
by the satellite. In these RTK systems, the distance between
the base stations and the rovers is a function of the phase
difference between the two antennas. As a result, the key
issue in using differential corrections lies in being able to tell
the difference between cycles of the carrier, or ambiguity
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resolution. While all manufacturers are constrained by the
physics of the situation to producing receivers that solve the
same sort of problem in the same way, there are considerable
differences in the software that is used for ambiguity
resolution, which mean that some receivers work much better
than others in a given situation.
Another consideration for optimal field work conditions is the
use of mission planning software. Since navigation satellites
are constantly moving in orbit, mission planning tools factor
in the location of the satellites to determine the most suitable
times for performing high-accuracy data collection in specific
locations where the most satellites will be visible and trackable.
Accuracy Across ScalesAsset managers have an increasing
number of position collection tools at
their disposal. There is a continuum of
technology, with associated increase in
cost for more accurate tools with higher
degrees of contextual information.
At the very low end, GPS receivers in
mobile phones offer a data collection
option with little accuracy and little means
to collect any details other than position
alone. On the high end, mobile LiDAR
sensors collect a great deal of positioninformation quickly with a high degree of
accuracy. LiDAR collection provides near
realism at high cost, with large volumes
of data that have associated costs of data
storage and the need to purchase software systems that can
process the data.
Most asset managers needs lie between these two extremes,
with requirements varying depending on the scope and scale
of work. Some will need to provide more accurate data for
small areas while others seek to cover larger scale regions
with better precision.
There are different price/performance ratios for different
levels of accuracy as well as considerations for different
scales of work. Many projects will require a hybrid approach
that harness varying degrees of positional accuracy for
their data collection applications. Increasingly, the use of a
central repository for this information within a geographic
information system (GIS) means that all the data collected will
be used repeatedly by a great many users.
The handheld approach is the standard for field workers for its
relatively low cost, high performance and adaptability. It can
provide suitable navigation and positional accuracy needed
while also supporting the extended geographic information
system (GIS) needs.
The value of high-quality data increases when its used for
a variety of uses such as transportation, utilities, housing,
construction, architecture and other infrastructure applications
that apply their tools, craft and data atop this foundation. For
example, an architect who wishes to present a high quality
design needs high quality spatial data upon which to orient a new
building, bridge or railway. Insurance professionals assessing risk
factors for flood plains are interested not only in climate history,
but 3D measurements of topography
that help them to see and understand
the relationship of buildings and other
infrastructure in terms of flood potential.
In addition to accuracy, the data pedigree
becomes important so that the multiple
users of the data have the ability to see
how the data was collected and can
ascertain its fitness for their purpose.
The ability to record metadata on the
positions that are collected, including
a photo for context and the device and
techniques that were deployed to recordthe position, give a needed trust for the
multiple use cases of the data.
When you come back to make a decision
on a position in five years time, its important to have a sense
of the quality of that position based on the details that you
recorded at the point of collection. Elements of trust regarding
data are particularly needed in a data sharing environment
in a large organization with lots of assets and multiple
departments accessing those assets, such as a large utility
or a municipality. Another important use for metadata is the
common need to merge datasets, particularly in our world ofconstant mergers and acquisitions, where data compatibility
and usability is dependent on its integrity.
Data at the Point of ExplorationMobile position platforms, such as handheld GNSS receivers,
provide data and exploration tools to the point of contact and
interaction. The handheld device is very relevant in a hybrid
position collection world, and is even superior in some cases,
because it provides attribute information in the hands of the
professionals doing the work.
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There remains a superiority of the human as a sensor for
details that couldnt be collected in any other way. Mobile
LiDAR is a forward-facing technology that cant see behind
signs and obstructions. Aerial imagery has a greater degree
of precision, but cant read the fine print of serial numbers
and discern asset types at high resolution. The human
sensor at the point of contact is ideal for this type of work,
and the handheld GNSS receiver fills a much-needed gap
in data collection. The addition of digital cameras within
the collection devices, such as the 5 megapixel camera in
Trimbles GeoExplorer 6000 Series GNSS handheld, adds to
the human sensor benefit by capturing close-up images as a
visual reinforcement of data pedigree.
In light of the need for higher accuracy, handheld solutions
are evolving to capture high-accuracy positions through a
variety of different means. The new Trimble GeoExplorer 6000
series GeoXH handheld takes a next step for collecting and
maintaining high-accuracy GNSS data in the field, with real-
time decimeter (10 cm / 4 inch) to subfoot (< 30 cm) accuracy,
and integrated Internet connectivity options that allow for two-
way connections between the office and the field.
Part of the issue of outfitting field workers with the highest-
accuracy device possible is one of productivity. When
youre sending a work crew out, you want to be sure that
they are collecting data wherever they are. GNSS yield andproductivity in tough environments such as in urban canyons
and under tree canopy are addressed through the latest
advancements with access to multiple satellite constellations,
and other approaches.
The added signals aid those workers in an urban setting, where
it isnt as difficult to revisit a site, but where speed of collection
is imperative due to safety and an expected pace of work.
Workers in remote areas appreciate the added signals and
speed to lock on a position, because its not as easy to go back.
This new level of accuracy in a lower-cost and accessible
device means that workflows are more streamlined without
the need for a separate trip to the work site by a surveyor.
Some surveying workflows are actually detrimental for
reliable positions, because there is often a multi-day lag
between when things are staked and worked on or when they
can collect data from newly dug underground infrastructure.
For instance, if the job is to dig a trench to get to buried
utilities, the workers can find the trench with a highly accurate
device, dig the trench, place and record the position of the
necessary operation, and fill the trench back in without having
to leave it open for some time until the surveyor can visit. The
worker is thus empowered to deal with the full scope of work
while still maintaining an accurate and trusted position that
can be relied upon in the future.
With a focus on the efficiency of the workforce, you enable a
whole new flexibility while also safeguarding the quality and
accuracy of the position data about your assets. By placing
high-accuracy into the hands of workers doing the work, there
are no multi-day lags between position collection, and reliable
high-accuracy position is tied much more closely with the
work.
These latest-generation handheld GNSS receivers are at the
nexus of all other collection means, as it provides a means to
visualize data that has already been collected, to collect new
data against established positions, and also to collect high-accuracy positions and attributes where no other position
baseline has been set.
High-Accuracy MandateThe U.S. Rail Safety Improvement Act of 2008 that passed
shortly after the deadly commuter rail accident in Los Angeles
mandates a whole new level of positional accuracy in order to
improve rail transportation automation. The federal rail safety
program for Positive Train Control (PTC) on the Class 1 freight
rails in North America mandates that each train be tracked,
and that no trains may travel on a track without its location
being known.
There are significant technological hurdles for the railways
to address before compliance with PTC becomes mandatory
in 2015. The requirements of the system are to prevent an
over-speed through automated engine controls, to avoid
signal violations, to protect track crews, to prevent movement
through a switch even if it is in the wrong position, and to
know the location of every rail asset within 2.2 meters of
horizontal accuracy and 0.8 meters vertical.
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Achieving the location requirement is a difficult task given that
the accuracy requirement is below todays mapping-grade
GPS, and the PTC requirement specifies that the location of
all assets must be maintained to 100% accuracy at all times.
To meet these requirements, the four major Class 1 North
American rail companies have banded together to specify
guidelines for an interoperable system, and each is actively
surveying all of their assets.
There are hurdles here due to an old hierarchical structure that
has divided operations by asset class, with departments for
signals, track, locomotives, etc. Each asset operation has been
responsible for recording their own details, including the location
of the asset, and the accuracy of those positions vary widely.
Surveying the entire tracks of each railroad to assay all assets
is a considerable project, given the miles of track, and the by-
and-large non-technically savvy crew. CSX, one of the major
East coast operators, has devised a plan that involves a mobile
handheld GPS data collection and a custom program that
was created jointly by Trimble and Esri. The solution was built
with both Esri ArcPad and Trimble GPS Pathfinder Software
Developer Kit, and contains rigid processing rules, relying on
differential correction to achieve the necessary accuracy.
The railroads are using a breadth of different geospatial tools
in order to meet this mandate. On the survey side are mobilesolutions, such as described above, as well as LiDAR data
collection via a high rail vehicle that travels all along their
tracks and a helicopter that collects both aerial LiDAR and
aerial imagery. LiDAR provides the detail and accuracy to
verify field data collection, and is also a good enough accuracy
for cataloging a large number of the assets. However, theyre
also ground verifying all assets, because there are some
things that the sensors cant pick up, like the DOT number on
a road crossing. Similarly, there are other details on signage
that cant be read and recorded other than by field crews.
The hybrid approach is a necessity due to these and other
limitations. The LiDAR collects precision at high speeds, but
can be obscured, and wont give a 360-degree view. Driving
the high rail on the track with video and imagery provides a
means to record speed signs and mile posts and other ground
attribution that you cant get from aerial. Field crews are also
needed to verify position and cross check the items collected.
This aggressive mandate of Positive Train Control is speeding
along many geospatial advancements due to the challenges
that it poses. All the tools and technology to make such a
system are available, yet the mandate pushes the boundaries
in terms of both the scale and accuracy that are required, as
well as the communication and coordination between multiple
competing companies that must share their databases in real
time in order to meet the safety requirement.
The handheld component of this project is at the center of all
data integration, because field-verified information becomes
the most trusted source, referencing and relying on the high
precision of the automated solutions.
Return on High-Accuracy InvestmentE.ON, a Germany-based energy company that is pursuing
a global presence in both renewable and non-renewable
power generation runs a 110kV high-voltage grid ranging
from Schleswig-Holstein to Bavaria with both above and
below-ground cables. The company also runs communicationcables for the remote control of transformer stations. Over the
years, the company has mapped their cable networks, but the
procedure and communication has not been consistent when
changes in the cable networks were made.
Lack of knowledge about changes has often led to cable
disturbances, because the location of telecommunication
cables were not recorded or communicated. It has also been
difficult to fix these disturbances or cable movements without
adequate location details to direct repair crews. The lack of
information added cost and time to maintenance and also
involved safety and risk management concerns.
During the last quarter of 2009, E.ON decided to pursue a
handheld GNSS solution based upon the accessible price of
Trimble GeoXH handhelds versus survey equipment or contract
surveyors. The company purchased virtual reference stations
(VRS) service in addition in order to reach sub-meter accuracy.
The E.ON requirement is to locate cable lines within 30- 40 cm,
the width of a shovel of a small excavator. The needed accuracy
is provided through the handhelds and this service, avoiding
the need to hire external surveying companies.
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Using GeoXH handhelds, the field personnel can collect
data for cables and cable re-allocations, coupling locations,
subsurface splitters, junctions and other important
infrastructure information. This technology enables the
incremental improvement of existing documentation, for
recording changes and improvements to the network, and for
communicating infrastructure plans.
With these easy-to-use devices in the vehicle, workers can
examine and collect the differences between the plan and the
reality. When a disturbance in the line occurs, the changes to
the lines are recorded, noted and saved after the repair work is
completed.
Only one accurate map of the network is needed, and the
ability to have correct coordinates on hand during regular
work saves time and the expense of external contractors.
Having high-accuracy information at hand also leads to
improved safety and the risk of damages to cables during field
work considerably reduced or prevented.
With improved geospatial information, it is now also possible
to present the coordinates for work digitally, without the need
for paper plans, communicating the locations of E.ON cable
lines via email or Google Earth, saving time.
Future Potential
With advancements in high-accuracy in a portable formfactor at a low cost, handhelds will continue to be central
to data accuracy improvements. Pervasive access to spatial
data provides information within the context of the work and
without the need for added specialists. The portable device is
easily accessible, allowing field workers front-line input into
position quality as well as the data and workflows tied to their
day-to-day work.
Theres an efficiency with an integrated handheld device
that has a camera, bar code scanner, and various custom
applications. Trying to integrate position with photos and
other attribute data in a non-integrated device has proven time
consuming and difficult, particularly in the context of fieldwork
or for automated collection scenarios such as mobile LiDAR
where the van is rolling by at 95 kilometers per hour (60 miles
per hour).
There is also the potential for the development of cloud
services that integrate large volumes of actionable
geospatial data over the Internet. The concept of a public
land survey register (cadastre) at a country scale with all
power, gas, telecommunication and utility lines and cables
available to all suppliers is within reach. This would lead
to a simplification and time-reduction when dealing with
construction inquiries and projects, with the data available
only to those that need access, and specific to the work at
hand.
The use of GNSS field devices is a solid element in building
out these kinds of digital infrastructure that would
connect wider groups of people together, result in greater
collaboration and improve upon communication while
reducing costs.
Technology is filling in the gaps in data accuracy without
requiring highly specialized users to come do this type ofwork, while also empowering users that arent experienced
surveyors or map experts. The trend toward more automated
high-accuracy data collection will continue on pace with
improvements in systems to share and collaborate around
work that has a strong spatial component.
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